Download or read book Chemometrics and Cheminformatics in Aquatic Toxicology written by Kunal Roy and published by John Wiley & Sons. This book was released on 2022-01-06 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY Explore chemometric and cheminformatic techniques and tools in aquatic toxicology Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms. You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods. Readers will also benefit from the inclusion of: A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining An exploration of aquatic toxicity databases, chemometric software tools, and webservers Practical examples and case studies to highlight and illustrate the concepts contained within the book A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.

Download or read book In Silico Toxicology written by Mark T. D. Cronin and published by Royal Society of Chemistry. This book was released on 2010 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book defines the use of computational approaches to predict the environmental toxicity and human health effects of organic chemicals.

Download or read book Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment written by Kunal Roy and published by Academic Press. This book was released on 2015-03-03 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. - Includes numerous practical examples related to QSAR methods and applications - Follows the Organization for Economic Co-operation and Development principles for QSAR model development - Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools

Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Download or read book Predictive Toxicology written by Christoph Helma and published by CRC Press. This book was released on 2005-03-17 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies, algorithms, and prediction methods to select, calculate, and represent the features and properties of chemical structures in biological systems. It provides sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry, biology, statistics, and data mining. Predictive Toxicology explores applications that go beyond classical structure-activity relationships and discusses programs such as OncoLogic, META, MC4PC, PASS, and lazar.

Download or read book Imbalanced Learning written by Haibo He and published by John Wiley & Sons. This book was released on 2013-06-07 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Download or read book Drug like Properties Concepts Structure Design and Methods written by Li Di and published by Elsevier. This book was released on 2010-07-26 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be less desirable as a drug product. This book is a tool and resource for scientists engaged in, or preparing for, the selection and optimization process. The authors describe how properties affect in vivo pharmacological activity and impact in vitro assays. Individual drug-like properties are discussed from a practical point of view, such as solubility, permeability and metabolic stability, with regard to fundamental understanding, applications of property data in drug discovery and examples of structural modifications that have achieved improved property performance. The authors also review various methods for the screening (high throughput), diagnosis (medium throughput) and in-depth (low throughput) analysis of drug properties. - Serves as an essential working handbook aimed at scientists and students in medicinal chemistry - Provides practical, step-by-step guidance on property fundamentals, effects, structure-property relationships, and structure modification strategies - Discusses improvements in pharmacokinetics from a practical chemist's standpoint

Download or read book Deep Learning for the Life Sciences written by Bharath Ramsundar and published by O'Reilly Media. This book was released on 2019-04-10 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Download or read book Mechanistic Toxicology written by Urs A. Boelsterli and published by CRC Press. This book was released on 2007-03-23 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough understanding of cellular and molecular mechanisms involved in the individual expression of toxic effects provides an important tool for assessment of human health risk. New aspects, major advances, and new areas in molecular and cellular biology and toxicology demand updated sources of information to elucidate the functional mechanics of human toxicology. Mechanistic Toxicology: The Molecular Basis of How Chemicals Disrupt Biological Targets, Second Edition retains the accessible format of the original to present the general principles that link xenobiotic-induced toxicity with the molecular pathways that underlie these toxic effects. Extensively illustrated, this book forms a conceptual bridge between multiple events at the molecular level and the determinants of toxicity at the physiological and cellular level. Specific examples of drugs, environmental pollutants, and other chemicals are carefully chosen to illustrate and highlight the fundamental mechanisms of toxicity at different toxicokinetic and toxicodynamic levels. The book includes references and review articles at the end of each chapter, as well as boxed text for relevant review information on biological, biochemical, molecular, and toxicological background. Linking molecular pathways to more general biomedical contexts, the author ensures that the reader is not lost in the details and instead receives a broad understanding of the processes underlying xenobiotic toxicity. New in the Second Edition Updated chapters Types of toxic responses Disruption of signal transduction by xenobiotics Disruption of mitochondrial function Novel mechanisms derived from systems toxicology

Download or read book High Throughput Screening Assays in Toxicology written by Hao Zhu and published by Humana. This book was released on 2023-03-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition volume expands on the previous edition by exploring the latest advancements in high throughput screening (HTS) in toxicity studies by using in vitro, ex vivo, and in vivo models. This volume also covers the application of artificial intelligence (AI) and data science to curate, manage, and use HTS data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, High Throughput Screening Assays in Modern Toxicology, Second Edition is a valuable resource for scientists pursuing chemical toxicology research. This book will aid scientists and researchers in translating new HTS techniques into standardized chemical toxicology assessment tools that can refine, reduce, and replace animal testing.

Download or read book Machine Learning and Deep Learning in Computational Toxicology written by Huixiao Hong and published by Springer Nature. This book was released on 2023-03-11 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.

Download or read book Artificial Intelligence in Drug Design written by Alexander Heifetz and published by Humana. This book was released on 2022-11-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

Download or read book Machine Learning in Biological Sciences written by Shyamasree Ghosh and published by Springer Nature. This book was released on 2022-05-04 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.

Download or read book New Technologies for Toxicity Testing written by Michael Balls and published by Springer Science & Business Media. This book was released on 2012-03-22 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central theme running through this volume on New Technologies for Toxicity Testing is the development and application of advanced techniques for cell and tissue culture, as well as new markers and endpoints of toxicity, as alternatives to the traditional paradigm of relying on data from laboratory animal tests to undertake labelling and risk assessment. Of course, many of the techniques and methods described in this volume are in the early stages of development, and much work will be needed to ensure their further improvement, optimisation and validation. However, we are confident that this will be achieved and that, just as with the in vitro assays that were validated and granted regulatory acceptance over the last decade, these, and many other new, advanced methods, will likewise become part of the toxicologist’s improved toolbox for coping with increasingly stringent and numerous regulatory requirements and test chemicals, while placing less reliance on traditional testing paradigms.

Download or read book Drug Induced Liver Toxicity written by Minjun Chen and published by Humana Press. This book was released on 2018-03-21 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive view of the methodologies used for the study of liver toxicity encountered throughout the whole life cycle of a drug, from drug discovery, to clinical trial, post-marketing, and even clinical practice. Organized into six sections, the first section introduces the mechanisms contributing to drug-induced liver toxicity. The second and third section explore in silico and in vitro approaches used to help mitigate hepatotoxicity liability at the early stages of drug development. The fourth section describes methodologies applied in regulatory processes, including preclinical studies, clinical trials, and post-marketing surveillance. The fifth section discusses clinical hepatotoxicity. Emerging technologies are examined in the final section. As a volume in the Methods in Pharmacology and Toxicology series, chapters include the kind of expert advice that will lead to optimal results. Authoritative and practical, Drug-Induced Liver Toxicity serves all those who aim to improve assessment and understanding of hepatotoxic potentials of new medications and marketed drugs. Chapter 30 is open access under a CC BY 4.0 license via link.springer.com.

Download or read book Computational Nanotoxicology written by Agnieszka Gajewicz and published by CRC Press. This book was released on 2019-11-13 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of computational methods that support human health and environmental risk assessment of engineered nanomaterials (ENMs) has attracted great interest because the application of these methods enables us to fill existing experimental data gaps. However, considering the high degree of complexity and multifunctionality of ENMs, computational methods originally developed for regular chemicals cannot always be applied explicitly in nanotoxicology. This book discusses the current state of the art and future needs in the development of computational modeling techniques for nanotoxicology. It focuses on (i) computational chemistry (quantum mechanics, semi-empirical methods, density functional theory, molecular mechanics, molecular dynamics), (ii) nanochemoinformatic methods (quantitative structure–activity relationship modeling, grouping, read-across), and (iii) nanobioinformatic methods (genomics, transcriptomics, proteomics, metabolomics). It reviews methods of calculating molecular descriptors sufficient to characterize the structure of nanoparticles, specifies recent trends in the validation of computational methods, and discusses ways to cope with the uncertainty of predictions. In addition, it highlights the status quo and further challenges in the application of computational methods in regulation (e.g., REACH, OECD) and in industry for product development and optimization and the future directions for increasing acceptance of computational modeling for nanotoxicology.